Singapore University of Social Sciences

Foundation to Python for AI

Foundation to Python for AI (AIB503)

Applications Open: 01 May 2024

Applications Close: 15 June 2024

Next Available Intake: July 2024

Course Types: Modular Graduate Course

Language: English

Duration: 6 months

Fees: $2200 View More Details on Fees

Area of Interest: Business Administration

Schemes: Alumni Continuing Education (ACE)

Funding: To be confirmed

School/Department: School of Business


Artificial intelligence (AI) has been transforming the way everyone lives, studies, works, and connects. This course AIB503 Foundation to Python for AI is designed to equip students with the knowledge in AI in order to embrace the technological revolution and paradigm shift. Python is an essential programming language in the toolkit of an AI professional. In this course, you will learn the essentials of Python programming in data management, data analytics, and data visualisation. By the end of the course, you should be able to understand the concepts of machine learning and deep learning and differentiate supervised and unsupervised learning. Next, students will learn to execute and implement AI models (e.g., regression and classification) to solve real-life problems. Last, examples and hands-on exercises will be designed to help students learn to visualise and present the machine learning results using Python toolkits, e.g., NumPy, SciPy, Pandas, Seaborn and Matplotlib.

Level: 5
Credit Units: 5
Presentation Pattern: EVERY REGULAR SEMESTER


  • Introduction to Python programming (basic methodology, syntax, logic, etc.)
  • Data types (tuple, list, dictionary)
  • Numpy basics (array, module, method, function)
  • Pandas and dataframe
  • Data collection (importing and storing data, web scraping, etc.)
  • Data preparation (manipulation, cleaning, transforming, merging, etc.)
  • Plotting and visualisation
  • Regression vs classification
  • Unsupervised learning
  • Artificial neural network
  • Convolutional neural network
  • Python for NLP basics (tokenisation, stemming, lemmatisation)

Learning Outcome

  • Appraise the fundamental methodology in Python programming
  • Evaluate supervised and unsupervised learning, regression and classification problems
  • Assess the applications for AI models
  • Prepare data for analysis using Python
  • Analyse data using appropriate tools and techniques with Python
  • Design and implement various AI models using Python
Back to top
Back to top